A parallel neural network approach to prediction of Parkinson's Disease

Authors:
Freddie ström;Rasit Koker
Affiliations:
Computer Vision Laboratory, Department of Electrical Engineering, Linköping University, SE-58183 Linköping, Sweden;Engineering Faculty Esentepe Kampus, Computer Engineering Department, Sakarya University, 54187 Sakarya, Turkey and Faculty of Engineering and Natural Sciences, Department of Computer Engineering, ...
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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Abstract

Recently the neural network based diagnosis of medical diseases has taken a great deal of attention. In this paper a parallel feed-forward neural network structure is used in the prediction of Parkinson's Disease. The main idea of this paper is using more than a unique neural network to reduce the possibility of decision with error. The output of each neural network is evaluated by using a rule-based system for the final decision. Another important point in this paper is that during the training process, unlearned data of each neural network is collected and used in the training set of the next neural network. The designed parallel network system significantly increased the robustness of the prediction. A set of nine parallel neural networks yielded an improvement of 8.4% on the prediction of Parkinson's Disease compared to a single unique network. Furthermore, it is demonstrated that the designed system, to some extent, deals with the problems of imbalanced data sets.